Purpose: To develop a deep‐learning system for identifying five dental implant brands from periapical radiographs and compare its diagnostic accuracy with dental professionals and evaluate successive You Only Look Once (YOLO) architectures (v7–v12) to justify model selection. Materials and Methods: A dataset of 5851 periapical radiographs was compiled and divided into training, validation, and test partitions (80/10/10). After filtering to five brands (Adin, Dentium, Noris, OSSTEM, and Straumann), YOLO‐based object detection models (versions 7–12) were trained and tested under identical conditions. The YOLOv12x model was adopted for final evaluation based on its optimal balance of accuracy and inference speed. Human performance was assessed using 100 held‐out test images (20 per brand) via a multiple‐choice web survey distributed to six clinician groups. Diagnostic metrics included mean average precision at IoU 0.50 (mAP@50), mAP@50–95, precision, recall, and accuracy. Results: The model achieved an mAP@50 of 0.989 (98.9%), an mAP@50–95 of 0.900 (90.0%), a precision of 0.969 (96.9%), and a recall of 0.977 (97.7%) across brands. Across YOLO generations, performance improved from mAP@50–95 = 0.817 (YOLOv7) to 0.905 (YOLOv12x). Fifty‐two clinicians completed 5,200 image evaluations; the model significantly outperformed all clinician subgroups (one‐way ANOVA with Tukey HSD, p < 0.001). Transformer‐based DF‐DETR achieved mAP@50–95 = 0.878, confirming YOLOv12x's superior efficiency–accuracy trade‐off. Conclusions: A high‐performing model identified implant brands on periapical radiographs and outperformed clinicians across experience levels. Comparative analysis across YOLO architectures validated its measurable advantage in accuracy and speed. Lack of external validation and dataset imbalance are important limitations; future work will include external, multisite data and human–AI workflow evaluation.

The accurate alignment of digital scan data is a critical step in providing a complete arch implant-supported prosthesis, mainly because of the lack of stable and repeatable reference points in edentulous arches. This limitation poses a significant challenge when attempting to align multiple surface geometry datasets precisely. This clinical report describes a practical and efficient procedure that enhanced the accuracy of digital dataset alignment and facilitated a fully digital workflow for the design, fabrication, and conversion of interim prostheses into definitive complete arch implant-supported prostheses.

Objective: Several studies have compared the accuracy of digital scans and conventional impressions for post space capture. However, only a few have specifically investigated the precision of intraoral scanners in measuring post spaces of varied lengths. This study aimed to evaluate the accuracy of various intraoral scanning techniques in capturing long and short post spaces. Material and Methods: This study grouped samples into eight categories based on four techniques and two post space depths (7 and 11 mm). After tooth preparation, root canal treatment, and post space preparation, laboratory scans were performed using Duralay. Intraoral scans were obtained directly and indirectly with the Trios fourth generation scanner using the Duralay and PVS techniques. The accuracies, in terms of trueness, and precisions were compared after ten repetitions for each group using the Kruskal–Wallis or Mann–Whitney U tests. Results: The Duralay Intraoral Scan groups demonstrated a high consistency, while the Direct Intraoral Scan groups showed moderate consistency. Variability was higher for the Duralay Lab Scan and PVS Intraoral Scan groups for short post spaces. Conclusions: The capture technique significantly affected the accuracies of the post space measurements. The techniques also demonstrated varying consistency and precision. These findings provide critical insights to guide their selections for clinical and research applications. Clinical Significance: This study is one of the few to compare the accuracy of intraoral scanners for the capture of both short and long post spaces. It addresses a key gap in current dental research and offers practical guidance for clinicians and researchers in selecting appropriate scanning techniques for various clinical scenarios. The findings have the potential to enhance the accuracy and reliability of post space measurements and improve patient outcomes.

Purpose: The present study investigated the combined influence of four different scanning patterns and two intraoral scanning (IOS) technologies (Confocal Ultrafast Optical Sectioning vs. Active Triangulation with Structured Light) on the trueness and precision of both the intaglio and cameo surfaces of a maxillary reference denture. Materials and Methods: A maxillary complete arch reference denture was scanned using IOS technologies: Confocal Ultrafast Optical Sectioning (CUOS) and Active Triangulation with Structured Light (ATSL). Scanning was done 80 times with four strategies: APBOP (starting on the posterior intaglio surface), PABOP (beginning at the palatal aspect of the intaglio surface), BOPAP (starting on the cameo surface), and POBAP (beginning at the palatal cameo surface). Each strategy involved thorough scanning of various aspects, including occlusal surfaces, to capture the complete dentate arch. Distance–deviation values were calculated from comparisons with the STL reference model, and the data were analyzed using two‐way ANOVA and Tukey's test. Results: For the CUOS IOS technology, the POBAP scanning pattern exhibited the highest trueness (161 ± 6 μm) and precision (151 ± 7 μm), whereas the APBOP pattern showed the lowest trueness (183 ± 28 μm) and precision (171 ± 33 μm). For the ATSL IOS technology, the POBAP pattern also demonstrated the highest trueness (200 ± 32 μm) and precision (198 ± 32 μm), whereas the PABOP pattern showed the lowest trueness (241 ± 24 μm) and precision (239 ± 24 μm). The main effect of scanning technology was statistically significant ( p < 0.001), with the estimated marginal mean (EMM) for CUOS being 171 μm, indicating greater trueness relative to ATSL (211 μm). A partial eta‐squared value of 0.52 indicated that scanning technology explained approximately 52% of the total variation in trueness, representing a substantial effect size. Conclusion: CUOS consistently exhibited superior trueness across all scanning patterns compared with ATSL.